ggplot2에 내장된 샘플데이터 mpg 정형화
# package
library(ggplot2) # 이 파일로 불러오는 것
df.mpg <- as.data.frame(ggplot2::mpg)
## 4함수 :: head, tail, str, summary
head(df.mpg)
tail(df.mpg)
str(df.mpg)
df.mpg %>%
data.table :: setnames(
old = "manufacturer",
new = "제조회사"
)
df.mpg %>%
data.table :: setnames(
old = "displ",
new = "배기량"
)
df.mpg %>%
data.table :: setnames(
old = "year",
new = "생산연도"
)
df.mpg %>%
data.table :: setnames(
old = "cyl",
new = "실린더개수"
)
df.mpg %>%
data.table :: setnames(
old = "trans",
new = "변속기종류"
)
df.mpg %>%
data.table :: setnames(
old = "drv",
new = "구동방식"
)
df.mpg %>%
data.table :: setnames(
old = "cty",
new = "도시연비"
)
df.mpg %>%
data.table :: setnames(
old = "hwy",
new = "고속도로연비"
)
df.mpg %>%
data.table :: setnames(
old = "fl",
new = "연료종류"
)
df.mpg %>%
data.table :: setnames(
old = "class",
new = "자동차종류"
)
df.mpg
summary(df.mpg)
df.mpg$total <- df.mpg$도시연비+df.mpg$고속도로연비
df.mpg$test <- ifelse(df.mpg$total >= 40, "high" , ifelse(df.mpg$total >= 30, "med", "low" ) )
table(df.mpg$test)
library(ggplot2)
ggplot2::qplot(df.mpg$test)
#'data.frame': 234 obs. of 11 variables:
# $ manufacturer (제조회사): chr "audi" "audi" "audi" "audi" ...
# $ model (모델) : chr "a4" "a4" "a4" "a4" ...
# $ displ (배기량) : num 1.8 1.8 2 2 2.8 2.8 3.1 1.8 1.8 2 ...
# $ year (생산연도) : int 1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
# $ cyl (실린더개수) : int 4 4 4 4 6 6 6 4 4 4 ...
# $ trans (변속기종류) : chr "auto(l5)" "manual(m5)" "manual(m6)" "auto(av)" ...
# $ drv (구동방식) : chr "f" "f" "f" "f" ...
# $ cty (도시연비) : int 18 21 20 21 16 18 18 18 16 20 ...
# $ hwy (고속도로연비) : int 29 29 31 30 26 26 27 26 25 28 ...
# $ fl (연료종류) : chr "p" "p" "p" "p" ...
# $ class (자동차종류) : chr "compact" "compact" "compact" "compact" ...
# > summary(df.mpg)
# manufacturer model displ year cyl
# Length:234 Length:234 Min. :1.600 Min. :1999 Min. :4.000
# Class :character Class :character 1st Qu.:2.400 1st Qu.:1999 1st Qu.:4.000
# Mode :character Mode :character Median :3.300 Median :2004 Median :6.000
# Mean :3.472 Mean :2004 Mean :5.889
# 3rd Qu.:4.600 3rd Qu.:2008 3rd Qu.:8.000
# Max. :7.000 Max. :2008 Max. :8.000
# trans drv cty hwy fl
# Length:234 Length:234 Min. : 9.00 Min. :12.00 Length:234
# Class :character Class :character 1st Qu.:14.00 1st Qu.:18.00 Class :character
# Mode :character Mode :character Median :17.00 Median :24.00 Mode :character
# Mean :16.86 Mean :23.44
# 3rd Qu.:19.00 3rd Qu.:27.00
# Max. :35.00 Max. :44.00
# class
# Length:234
# Class :character
# Mode :character
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